Meta Platforms Inc (META) 2024 Q4 法說會逐字稿

Good afternoon. My name is Krista and I will be your conference operator today.

午安.我叫克里斯塔 (Krista)，今天我將擔任您的會議主持人。

At this time, I would like to welcome everyone to the Meta's fourth-quarter and full year 2024 earnings conference call. (Operator Instructions)

現在，我歡迎大家參加 Meta 2024 年第四季和全年財報電話會議。（操作員指示）

And this called will be recorded.

並且這個呼叫將被記錄下來。

Kenneth Dorell, Meta's Director of Investor Relations, you may begin.

您可以請 Meta 投資者關係總監 Kenneth Dorell 開始。

Thank you. Good afternoon and welcome to Meta Platform's fourth-quarter and full year 2024 earnings conference call.

謝謝。下午好，歡迎參加 Meta Platform 2024 年第四季和全年財報電話會議。

Joining me today to discuss our results are Mark Zuckerberg, CEO; and Susan Li, CFO.

今天與我一起討論我們業績的有執行長馬克·祖克柏和財務長蘇珊·李。

Before we get started, I would like to take this opportunity to remind you that our remarks today will include forward-looking statements. Actual results may differ materially from those contemplated by these forward-looking statements. (technical difficulty) Earnings press release and in our quarterly report on Form 10-Q filed with the SEC. Any forward-looking statements that we make on this call are based on assumptions as of today and we undertake no obligation to update these statements as a result of new information or future events. During this call, we will present both GAAP and certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today's earnings press release. The earnings press release and an accompanying investor presentation are available on our website at investor.atmeta.com.

在我們開始之前，我想藉此機會提醒大家，我們今天的發言將包括前瞻性陳述。實際結果可能與這些前瞻性陳述所預期的結果有重大差異。（技術難題）收益新聞稿和我們向美國證券交易委員會提交的 10-Q 表季度報告。我們在本次電話會議上所做的任何前瞻性陳述均基於今天的假設，我們不承擔因新資訊或未來事件而更新這些陳述的義務。在本次電話會議中，我們將介紹 GAAP 和某些非 GAAP 財務指標。今天的收益新聞稿中包含了 GAAP 與非 GAAP 指標的對帳。收益新聞稿和隨附的投資者介紹可在我們的網站 investor.atmeta.com 上查閱。

And now I'd like to turn the call over to Mark.

現在我想把電話轉給馬克。

(technical difficulty) Ended 2024 on a strong note with now more than 3.3 billion people using at least one of our apps each day. This is going to be a really big year. I know it always feels like every year is a big year, but more than usual, it feels like the trajectory for most of our long term initiatives is going to be a lot clearer by the end of this year. So I keep telling our teams that this is going to be intense because we have about 48 weeks to get on the trajectory that we want to be on. In AI, I expect (technical difficulty) AI assistant reaches more than 1 billion people and I expect Meta AI to be that leading AI assistant.

（技術難題）2024 年以強勁勢頭結束，目前每天有超過 33 億人使用我們的至少一款應用程式。今年將會是意義非凡的一年。我知道每年都感覺是重要的一年，但與往常相比，感覺我們大多數長期計劃的軌跡到今年年底都會更加清晰。所以我一直告訴我們的團隊，這將是非常緊張的，因為我們有大約 48 週的時間來走上我們想要的軌道。在人工智慧方面，我預計（技術難度）人工智慧助理將覆蓋超過 10 億人，我預計 Meta AI 將成為領先的人工智慧助理。

Meta AI is already used by more people than any other assistant and once the service reaches that kind of scale, it usually develops a durable long term advantage. We have a really exciting road map for this year with a unique vision focused on personalization. We believe that people don't all want to use the same AI. People want their AI to be personalized to their context, their interests, their personality, their culture and how they think about the world.

Meta AI 的使用人數已經超過了任何其他助手，一旦服務達到這種規模，它通常會形成持久的長期優勢。我們今年有一個非常令人興奮的路線圖，其獨特願景專注於個人化。我們相信人們並不都想使用相同的人工智慧。人們希望他們的人工智慧能夠根據他們的環境、興趣、個性、文化以及他們對世界的看法進行個性化。

I don't think that there's just going to be one big AI that everyone uses that does the same thing. People are going to get to (technical difficulty) continue to think that this is going to be one of the most transformative products that we've made. And we have some fun surprises that I think people are going to like this year. I think this will very well be the year when Llama and open source become the most advanced and widely used AI models as well. Llama 4 is making great progress in training. Llama 4 mini is done with pretraining. And our reasoning models and larger model are looking good too.

我不認為會存在一個大型人工智慧，供所有人使用，做同樣的事情。人們將會克服（技術困難）繼續認為這將是我們製造的最具變革性的產品之一。我們還有一些有趣的驚喜，我認為人們今年會喜歡的。我認為今年 Llama 和開源很可能成為最先進、應用最廣泛的人工智慧模式。Llama 4 在訓練中取得了很大進展。Llama 4 mini 已完成預訓練。我們的推理模型和更大的模型看起來也不錯。

Our goal with Llama 3 was to make open source competitive with closed models and (technical difficulty) natively multi modal, it's an omni model and it will have agentic capabilities. So it's going to be novel and it's going to unlock a lot of new use cases. And I'm looking forward to sharing more of our plan for the year on that over the next couple of months.

我們對 Llama 3 的目標是使開源能夠與封閉模型競爭，並且（技術難度）原生多模式，它是一種全能模型，並且具有代理能力。所以它將是新穎的並且將解鎖許多新的用例。我期待在接下來的幾個月分享更多關於我們今年的計劃。

I also expect that 2025 will be the year when it becomes possible to build an AI engineering agent that has coding and problem solving abilities of around a good mid level engineer. And this is going to be a profound milestone. (technical difficulty) in history like as well as over time, potentially a very large market. Whichever company builds this first, I think is going to have a meaningful advantage in deploying it to advance their AI research and shape the field.

我還預計，到 2025 年，我們將能夠打造出一個人工智慧工程代理，其編碼和解決問題的能力將與優秀的中級工程師相當。這將成為歷史上一個意義深遠的里程碑（技術難題），隨著時間的推移，它可能會成為一個非常大的市場。無論哪家公司首先建立這個系統，我認為它都將在部署它以推進其人工智慧研究和塑造該領域方面具有顯著優勢。

So that's another reason why I think that this year is going to set the course for the future. Our Ray-Ban Meta AI glasses are a real hit. And this will be the year when we understand the trajectory for AI glasses as a category. Many breakout products (technical difficulty) and their third generation. This will be a defining year that determines if we're on a path towards many hundreds of millions and eventually billions of AI glasses. And glasses being the next computing platform like we've been talking about for some time or if this is just going to be a longer grind.

所以這是我認為今年將為未來設定方向的另一個原因。我們的雷朋 Meta AI 眼鏡非常受歡迎。今年我們將了解人工智慧眼鏡這一類別的發展軌跡。許多突破性產品（技術難度）及其第三代產品。今年將是決定我們是否能夠走向數億甚至數十億副人工智慧眼鏡的決定性一年。就像我們之前討論過的，眼鏡將成為下一個運算平台，但這是否只是一段較長的歷程。

But it's great overall to see people recognizing that these glasses are the perfect form factor for AI as well as just great stylish glasses. These are all big investments, especially the hundreds of billions of dollars that we will invest in AI infrastructure over the long term. I announced last week that we expect to bring online almost a gigawatt of capacity this year and we're building a 2 gigawatt and and potentially bigger AI data center that is so big that it will cover a significant part of Manhattan if we were placed there.

但總的來說，很高興看到人們認識到這些眼鏡是人工智慧的完美外形，也是一款非常時尚的眼鏡。這些都是大投資，特別是我們將長期投資數千億美元用於人工智慧基礎設施。我上週宣布，我們預計今年將上線近 1 千兆瓦的容量，並且我們正在建造一個 2 千兆瓦甚至更大的人工智慧資料中心，這個資料中心非常大，如果我們把它安置在那裡，它將覆蓋曼哈頓的很大一部分。

We're planning to fund all of this by at the same time investing aggressively in initiatives that use these AI advances to increase revenue growth. And we've put together a plan that will hopefully accelerate the pace of these initiatives over the next few years. That's what a lot of our new headcount growth is going towards. And how well we execute on this will also determine our financial trajectory over the next few years.

我們計劃透過積極投資利用這些人工智慧進步來增加收入成長的計劃來為所有這些項目提供資金。我們制定了一項計劃，希望能夠在未來幾年加快這些措施的步伐。這就是我們大量新員工成長的目的。我們執行得如何也將決定我們未來幾年的財務軌跡。

There are a number of other important product trends related to our family of apps that I think we're going to know more about this year as well. We're going to learn what's going to happen with TikTok and regardless of that, I expect reels on Instagram and Facebook to continue growing. I expect Threads to continue on its trajectory to become the leading discussion platform and eventually reach 1 billion people over the next several years.

還有許多其他與我們的應用程式系列相關的重要產品趨勢，我認為我們今年也會對這些趨勢有更多的了解。我們將了解 TikTok 的未來發展，無論如何，我預計 Instagram 和 Facebook 上的 Reels 將繼續成長。我預計 Threads 將繼續沿著其軌跡發展，成為領先的討論平台，並最終在未來幾年內覆蓋 10 億用戶。

Threads now has more than 320 million monthly actives and has been adding more than 1 million sign ups per day. I expect WhatsApp to continue gaining share and making progress towards becoming the leading messaging platform in the US like it is in a lot of the rest of the world. WhatsApp now has more than 100 million monthly actives in the US. Facebook is used by more than 3 billion monthly actives and we're focused on growing its cultural influence and I'm excited this year to get back to some og Facebook.

目前，Threads 每月活躍用戶超過 3.2 億，並且每天新增註冊用戶超過 100 萬。我預計 WhatsApp 將繼續獲得市場份額，並朝著成為美國領先的訊息平台的方向發展，就像它在世界其他許多地方一樣。WhatsApp 目前在美國每月擁有超過 1 億活躍用戶。Facebook 每月有超過 30 億活躍用戶，我們致力於擴大其文化影響力，我很高興今年能夠重返 Facebook。

All right. So this is also going to be a pivotal year for the metaverse. The number of people using Quest and Horizon has been steadily growing. And this is a year when a number of the long term investments that we've been working on that will make the metaverse more visually stunning and inspiring, will really start to land. So I think we're going to know a lot more about Horizon's trajectory by the end of this year.

好的。因此，這也將是元宇宙的關鍵一年。使用 Quest 和 Horizo​​n 的人數一直在穩步增長。今年，我們致力於的一系列長期投資將開始真正落地，這些投資將使元宇宙在視覺上更加令人驚嘆和鼓舞人心。所以我認為到今年年底我們將對 Horizo​​n 的發展軌跡有更多了解。

This is also going to be a big year for redefining our relationship with governments. We now have a US administration that is proud of our leading companies, prioritizes American technology winning and that will defend our values and interests abroad. And I am optimistic about the progress and innovation that this can unlock.

這也將是重新定義我們與政府關係的重要一年。我們現在的美國政府為我們領先的公司感到自豪，優先考慮美國技術的勝利，並將捍衛我們在海外的價值觀和利益。我對此能帶來的進步和創新充滿樂觀。

So this is going to be a big year. I think that this is the most exciting and dynamic that I have ever seen our industry. Between AI, glasses, massive infrastructure projects, doing a bunch of work to try to accelerate our business and building the future of social media, we have a lot to do. And I think we're going to build some awesome things that shape the future of human connection. As always, I am grateful for everyone who's on this journey with us.

所以今年將是重要的一年。我認為這是我見過的我們行業最令人興奮、最有活力的一次。在人工智慧、眼鏡、大型基礎設施項目、為加速業務而開展的大量工作以及建立社交媒體的未來之間，我們有很多事情要做。我認為我們將會創造一些令人驚嘆的事物來塑造人類連結的未來。一如既往，我感謝與我們一起踏上這段旅程的每一個人。

Thank you.

謝謝。

And here's Susan.

這是蘇珊。

Thanks, Mark and good afternoon, everyone.

謝謝，馬克，大家下午好。

Let's begin with our consolidated results. All comparisons are on a year over year basis unless otherwise noted. Q4 total revenue was $48.4 billion, up 21% on both a reported and constant currency basis. Q4 total expenses were $25 billion, up 5% compared to last year. Before I cover the specific cost lines, I would note that our fourth quarter expense growth rate reflects a 13 percentage point favorable impact from legal accrual reductions in Q4 and lower year over year restructuring costs.

讓我們從綜合結果開始。除非另有說明，所有比較均以同比計算。第四季總營收為 484 億美元，按報告和固定匯率計算均成長 21%。第四季總支出為 250 億美元，比去年同期成長 5%。在介紹具體成本項目之前，我想指出，我們第四季度的費用增長率反映了第四季度法律應計費用減少和同比重組成本下降帶來的 13 個百分點的有利影響。

In terms of the specific line items, cost of revenue increased 15% driven mostly by higher infrastructure costs. R&D increased 16% primarily driven by higher employee compensation and infrastructure costs which were partially offset by lower restructuring costs. Marketing and sales were approximately flat year over year. G&A decreased 67% driven mostly by lower legal related expenses due to a $1.55 billion reduction in legal accruals related to certain legal proceedings.

就具體項目而言，收入成本增加了 15%，主要原因是基礎設施成本增加。研發費用增加了 16%，主要得益於員工薪資和基礎設施成本的提高，但重組成本的降低部分抵銷了這一成長。行銷和銷售與去年同期基本持平。一般及行政費用下降 67%，主要原因是與某些法律訴訟相關的法律應計費用減少了 15.5 億美元，導致法律相關費用降低。

We ended the year with over 74,000 employees, up 10% year over year with growth primarily driven by hiring and priority areas of monetization, infrastructure, generative AI, Reality Labs as well as regulation and compliance. Fourth quarter operating income was $23.4 billion, representing a 48% operating margin. Our tax rate for the quarter was 12%. Net income was $20.8 billion or $8.2 per share. Capital expenditures including principal payments on finance leases were $14.8 billion driven by investments in servers, data centers and network infrastructure.

截至今年年底，我們擁有超過 74,000 名員工，年增 10%，成長主要得益於招聘和貨幣化、基礎設施、生成式人工智慧、現實實驗室以及監管和合規等優先領域。第四季營業收入為 234 億美元，營業利益率為 48%。我們本季的稅率為 12%。淨收入為 208 億美元，即每股 8.2 美元。包括融資租賃本金支付在內的資本支出為 148 億美元，主要來自對伺服器、資料中心和網路基礎設施的投資。

Free cash flow was $13.2 billion. We paid $1.3 billion in dividends to shareholders, ending the year with $77.8 billion in cash and marketable securities and $28.8 billion in debt.

自由現金流為132億美元。我們向股東支付了 13 億美元的股息，年底時我們擁有 778 億美元的現金和有價證券以及 288 億美元的債務。

Moving now to our segment results. I'll begin with our family of apps segment. Our community across the family of apps continues to grow and we estimate more than 3.3 billion people used at least one of our family of apps on a daily basis in December. Q4 total family of apps revenue was $47.3 billion, up 21% year over year. And Q4 family of apps ad revenue was $46.8 billion, up 21% on both a reported and constant currency basis.

現在轉到我們的分部業績。我將從我們的應用程式系列部分開始。我們的應用程式系列社群正在不斷壯大，我們估計 12 月每天有超過 33 億人至少使用我們的一款應用程式系列。第四季應用程式總營收為 473 億美元，年增 21%。第四季應用程式系列廣告收入為 468 億美元，按報告和固定匯率計算均成長 21%。

Within ad revenue, the online commerce vertical was the largest contributor to year over year growth. On a user geography basis, ad revenue growth was strongest in rest of the world at 27% followed by Asia Pacific and Europe at 23% and 22% respectively. North America grew 18%. In Q4, the total number of ad impressions served across our services increased 6% and the average price per ad increased 14%. Impression growth was mainly driven by Asia Pacific. Pricing growth benefited from increased advertiser demand in part driven by improved ad performance. This was partially offset by impression growth particularly from lower monetizing regions and surfaces.

在廣告收入中，線上商務垂直領域是年成長的最大貢獻者。從用戶地理來看，全球其他地區的廣告收入成長最為強勁，為 27%，其次是亞太地區和歐洲，分別為 23% 和 22%。北美成長了18%。第四季度，我們各項服務的廣告展示總數增加了 6%，每個廣告的平均價格增加了 14%。印象成長主要受到亞太地區的推動。定價成長受惠於廣告商需求的增加，而廣告效果的改善是推動這一成長的部分原因。這被印象增長（特別是來自貨幣化程度較低的地區和表面的印象增長）部分抵消。

Family of apps other revenue was $519 million, up 55% driven primarily by business messaging revenue growth from our WhatsApp Business platform. We continue to direct the majority of our investments toward the development and operation of our family of apps. In Q4, family of apps expenses were $19 billion, representing 76% of our overall expenses. Family of apps expenses were up 5% primarily due to growth in infrastructure costs and employee compensation which were partially offset by lower legal related expenses.

應用程式系列的其他收入為 5.19 億美元，成長 55%，主要得益於我們的 WhatsApp Business 平台的商業訊息收入成長。我們繼續將大部分投資用於應用程式系列的開發和營運。第四季度，應用程式系列的支出為 190 億美元，占我們整體支出的 76%。應用程式系列費用上漲 5%，主要原因是基礎設施成本和員工薪資的成長，但法律相關費用的降低部分抵消了這一增長。

Family of apps operating income was $28.3 billion, representing a 60% operating margin. Within our Reality Lab segment, Q4 revenue was $1.1 billion driven by hardware sales and up 1% year over year. Reality Labs expenses were $6 billion, up 6% year over year driven primarily by higher infrastructure costs and employee compensation partially offset by lower restructuring costs. Reality Labs operating loss was $5 billion.

應用程式系列營運收入為 283 億美元，營運利潤率為 60%。在我們的現實實驗室部門，第四季營收為 11 億美元，由硬體銷售推動，年增 1%。Reality Labs 的支出為 60 億美元，年增 6%，這主要歸因於基礎設施成本和員工薪酬的增加，但重組成本的降低部分抵消了這一增長。Reality Labs 的營運虧損為 50 億美元。

Turning now to the business outlook. There are two primary factors that drive our revenue performance, our ability to deliver engaging experiences for our community and our effectiveness at monetizing that engagement over time. On the first, daily actives continue to grow across Facebook, Instagram and WhatsApp year over year both globally and in the United States.

現在談談商業前景。推動我們收入表現的主要因素有兩個：我們為社區提供引人入勝的體驗的能力，以及我們隨著時間的推移將這種參與貨幣化的有效性。首先，Facebook、Instagram 和 WhatsApp 的全球和美國每日活躍用戶數逐年持續成長。

In Q4, global video time grew at double digit percentages year over year on Instagram. And we're seeing particular strength in the US on Facebook where video time spent was also up double digit rates year over year. We see continued opportunities to drive video growth in 2025 through ongoing optimizations to our ranking systems. We're also making several product bets that are focused on setting up our platforms for longer term success.

第四季度，Instagram 的全球影片觀看時間年增了兩位數百分比。我們看到，美國的 Facebook 表現特別強勁，用戶觀看影片的時間也比去年同期成長了兩位數。我們看到，透過持續優化我們的排名系統，2025 年仍有機會推動影片成長。我們也對幾款產品進行了投資，重點是建立我們的平台以獲得長期成功。

Creators are one of our central focuses. On Instagram, we continue to prioritize original posts and recommendations to help smaller creators get discovered. We also want to ensure creators have a place to experiment with their content. So we introduced a new feature in Q4 that allows creators to first share a reel with people who don't follow them. This allows them to test content and see what performs best before deciding to share it with their followers and also helps introduce them to entirely new audiences.

創作者是我們關注的焦點之一。在 Instagram 上，我們繼續優先考慮原創貼文和推薦，以幫助小型創作者被發現。我們也希望確保創作者有一個地方來試驗他們的內容。因此，我們在第四季度推出了一項新功能，讓創作者先與未追蹤他們的人分享作品。這使得他們能夠在決定與追蹤者分享內容之前測試內容並了解哪些內容效果最佳，同時也有助於將他們介紹給全新的受眾。

Creative tools is another area we're investing in. In the coming weeks, we'll launch a new standalone app called Edits that provides a full suite of creative tools to make it easier for creators to make great reels on their phone. Another focus is making it easier for people to connect over content. Reels are already reshared over 4.5 billion times a day and we've been introducing more features that bring together the social and entertainment aspects of Instagram.

創意工具是我們投資的另一個領域。在接下來的幾週內，我們將推出一款名為 Edits 的新獨立應用程序，該應用程式提供全套創意工具，使創作者可以更輕鬆地在手機上製作精彩的捲軸。另一個重點是讓人們更容易透過內容進行聯繫。Reels 每天的轉發次數已超過 45 億次，我們也一直在推出更多功能，將 Instagram 的社交和娛樂功能結合在一起。

In the US, we recently launched a new destination in reels that consists of content your friends have left a note on or liked. We're seeing very positive early results and we'll look to expand this globally in the coming months. On Threads, we made tremendous progress in 2024 and our focus this year is establishing Threads as the place people come to keep up with what they care about.

在美國，我們最近推出了一個新的捲軸目的地，其中包含您的朋友留下的註釋或喜歡的內容。我們看到了非常積極的早期成果，並希望在未來幾個月內將其擴展到全球。在 Threads 方面，我們在 2024 年取得了巨大的進步，今年我們的重點是將 Threads 打造為人們關注他們所關心的事物的地方。

We're making a number of updates to our recommendation systems to prioritize more recent posts, surface content from top creators and ensure people see more of the content from accounts they follow. We will also continue improving custom feeds so people can build personalized feeds on topics they're interested in.

我們正在對推薦系統進行一系列更新，以優先顯示最近的貼文、展示來自頂級創作者的內容，並確保人們能夠看到更多他們關注的帳戶的內容。我們還將繼續改進自訂提要，以便人們可以根據自己感興趣的主題建立個人化提要。

Finally, Meta AI usage continues to scale with more than 700 million monthly actives. We're now introducing updates that will enable Meta AI to deliver more personalized and relevant responses by remembering certain details from people's prior queries and considering what they engage with on Facebook and Instagram to develop better intuition for their interests and preferences.

最後，Meta AI 的使用量持續擴大，每月活躍用戶超過 7 億。我們現在推出的更新將使 Meta AI 能夠透過記住人們先前查詢的某些細節並考慮他們在 Facebook 和 Instagram 上互動的內容來提供更個人化和相關的回應，從而更好地了解他們的興趣和偏好。

Now to the second driver of our revenue performance, increasing monetization efficiency. The first part of this work is optimizing the level of ads with an organic engagement. We continue to grow supply on lower monetizing surfaces like video while optimizing ad supply on each of our surfaces to deliver ads at the time and place they will be most relevant to people.

現在談談我們收入表現的第二個驅動力，即提高貨幣化效率。這項工作的第一部分是透過有機參與來優化廣告水平。我們繼續在影片等盈利能力較低的平台上增加供應，同時優化每個平台上的廣告供應，以便在與人們最相關的時間和地點投放廣告。

For example, we are continuing to better personalize when ads show up including the optimal locations in the depth of someone's feed, to introduce ads supply when it's most optimal for the user and revenue. This is enabling efficient supply growth. Longer term, we also see impression growth opportunities on unmonetized surfaces like Threads which we are beginning to test ads on this quarter. We expect the introduction of ads on Threads will be gradual and don't anticipate it being a meaningful driver of overall impression or revenue growth in 2025.

例如，我們將繼續更好地個人化廣告的顯示時間，包括在用戶資訊流深度中的最佳位置，以便在對用戶和收入最有利時引入廣告供應。這使得供應能夠高效成長。從長遠來看，我們也看到了非貨幣化平台（如 Threads）上的印象成長機會，我們本季開始在其上測試廣告。我們預計 Threads 上的廣告引入將是漸進的，並且預計它不會成為 2025 年整體印像或收入成長的顯著推動力。

The second part of increasing monetization efficiency is improving marketing performance. The ongoing enhancements to our ads ranking systems are an important driver of this work. In the second half of 2024, we introduced an innovative new machine learning system in partnership with NVIDIA called Andromeda. This more efficient system enabled a 10,000 times increase in the complexity of models we use for ads retrieval, which is the part of the ranking process where we narrow down a pool of tens of millions of ads to the few thousand we consider showing someone.

提升變現效率的第二部分是提升行銷績效。我們廣告排名系統的持續改進是這項工作的重要推動力。2024 年下半年，我們與 NVIDIA 合作推出了名為 Andromeda 的創新新型機器學習系統。這個更有效率的系統使我們用於廣告檢索的模型的複雜性提高了 10,000 倍，這是排名過程的一部分，我們將數千萬條廣告縮小到我們考慮向某人展示的數千條廣告。

The increase in model complexity is enabling us to run far more sophisticated prediction models to better personalize which ads we show someone. This has driven an 8% increase in the quality of ads that people see on objectives we've tested. Andromeda's ability to efficiently process larger volumes of ads also positions us well for the future as advertisers use our generative AI tools to create and test more ads. Another way we're delivering value for advertisers is through increased automation of their ad campaigns with Advantage+. Adoption of Advantage+ Shopping campaigns continues to scale with revenue surpassing a $20 billion annual run rate and growing 70% year over year in Q4.

模型複雜性的增加使我們能夠運行更複雜的預測模型，從而更好地個性化我們向某人展示的廣告。這使得我們測試過的目標上人們看到的廣告品質提高了 8%。隨著廣告主使用我們的生成式 AI 工具來創建和測試更多廣告，Andromeda 高效處理大量廣告的能力也為我們的未來做好了準備。我們為廣告主提供價值的另一種方式是利用 Advantage+ 來提高其廣告活動的自動化程度。Advantage+ 購物活動的採用規模持續擴大，第四季營收超過 200 億美元的年運行率，年增 70%。

Given the strong performance and interest we're seeing in Advantage+ Shopping and our other end to end solutions, we're testing a new streamlined campaign creation flow so advertisers no longer need to choose between running a manual or Advantage+ sales or app campaign. In this new setup, all campaigns optimizing for sales app or lead objectives will have Advantage+ turned on from the beginning.

鑑於 Advantage+ Shopping 和其他端到端解決方案的強勁表現和關注度，我們正在測試一種新的簡化廣告系列創建流程，以便廣告商不再需要在運行手動或 Advantage+ 銷售或應用廣告系列之間做出選擇。在這個新設定中，所有針對銷售應用程式或主要目標進行最佳化的廣告活動將從一開始就啟用 Advantage+。

This will allow more advertisers to take advantage of the performance Advantage+ offers while still having the ability to further customize aspects of their campaigns when they need to. We plan to expand to more advertisers in the coming months before fully rolling it out later in the year. Advantage+ Creative is another area where we're seeing momentum. More than 4 million advertisers are now using at least one of our generative AI ad creative tools, up from 1 million six months ago.

這將允許更多廣告主利用 Advantage+ 提供的效能，同時仍能在需要時進一步自訂其廣告活動的各個方面。我們計劃在未來幾個月內擴展到更多的廣告商，並在今年稍後全面推出。Advantage+ Creative 是我們看到發展動能的另一個領域。目前，已有超過 400 萬廣告商正在使用我們的至少一種生成式 AI 廣告創意工具，而六個月前這一數字為 100 萬。

There has been significant early adoption of our first video generation tool that we rolled out in October image animation with hundreds of thousands of advertisers already using it monthly.

我們在 10 月推出的首款影片產生工具——圖像動畫——已經獲得了廣泛的早期採用，每月已有數十萬廣告商使用它。

Next, I would like to discuss our approach to capital allocation. Our primary focus remains investing capital back into the business with infrastructure and talent being our top priorities.

接下來我想討論一下我們的資本配置方法。我們的首要重點仍然是將資本重新投入到業務中，而基礎設施和人才是我們的首要任務。

On the first, we expect compute will be central to many of the opportunities we're pursuing as we advance the capabilities of Llama, drive increased usage of generative AI products and features across our platform and fuel core ads and organic engagement initiatives. We're working to meet the growing capacity needs for these services by both scaling our infrastructure footprint and increasing the efficiency of our workloads. Another way we're pursuing efficiencies is by extending the useful lives of our servers and associated networking equipment.

首先，我們預期計算將成為我們所追求的許多機會的核心，因為我們將提升 Llama 的功能，推動整個平台上生成式 AI 產品和功能的使用，並推動核心廣告和有機參與計劃。我們正在努力透過擴大基礎設施規模和提高工作負載效率來滿足這些服務日益增長的容量需求。我們追求效率的另一種方式是延長伺服器和相關網路設備的使用壽命。

Our expectation going forward is that we'll be able to use both our non AI and AI servers for a longer period of time before replacing them, which we estimate will be approximately 5.5 years. This will deliver savings in annual CapEx and resulting depreciation expense which is already included in our guidance.

我們對未來的期望是，我們將能夠在更換非 AI 和 AI 伺服器之前使用它們更長的時間，我們估計這將大約為 5.5 年。這將節省年度資本支出和由此產生的折舊費用，這些費用已包含在我們的指導中。

Finally, we're pursuing cost efficiencies by deploying our custom MTIA silicon in areas where we can achieve a lower cost of compute by optimizing the chip to our unique workloads. In 2024, we started deploying MTIA to our ranking and recommendation influence workloads for ads and organic content. We expect to further ramp adoption of MTIA for these use cases throughout 2025 before extending our custom silicon efforts to training workloads for ranking and recommendations next year.

最後，我們透過在可以根據我們獨特的工作負載優化晶片來實現較低運算成本的領域部署我們的客製化 MTIA 矽片來追求成本效益。2024 年，我們開始將 MTIA 部署到廣告和自然內容的排名和建議影響工作負載。我們預計在 2025 年進一步擴大 MTIA 在這些用例中的應用，然後在明年將我們的客製化矽片工作擴展到排名和建議的訓練工作負載。

From a hiring standpoint, our focus continues to be on adding technical talent to support our strategic priorities. In the fourth quarter, nearly 90% of our year over year headcount growth was within the R&D function. The remaining growth was primarily in cost of revenue as we added an infrastructure headcount to support our data center operations. In 2025, we expect headcount growth will continue to be primarily driven by technical roles across our priority initiatives within infrastructure, monetization, Reality Labs, generative AI as well as regulation and compliance.

從招募的角度來看，我們的重點仍然是增加技術人才以支持我們的策略重點。第四季度，我們員工人數較去年同期成長的近 90% 都來自研發部門。剩餘的成長主要來自收入成本，因為我們增加了基礎設施員工來支援我們的資料中心營運。到 2025 年，我們預計員工人數的成長將繼續主要由基礎設施、貨幣化、現實實驗室、生成人工智慧以及監管和合規等優先計劃中的技術職位推動。

We anticipate headcount growth in our business functions will remain relatively limited. To achieve our ambitions in these areas, we will need to continue executing at a rapid pace. We're supporting this by building tools to help our engineering base be more productive. As part of our efficiency focus over the past two years, we've made significant improvements in our internal practices and developer tools and introduced new tools like our AI-powered coding assistant which is helping our engineers write code more quickly.

我們預計，我們業務職能部門的員工數量成長將保持相對有限。為了實現我們在這些領域的目標，我們需要繼續快速執行。我們正在透過建立工具來支持這一點，以幫助我們的工程基礎提高生產力。作為過去兩年效率重點的一部分，我們在內部實踐和開發人員工具方面取得了重大改進，並推出了人工智慧編碼助理等新工具，幫助我們的工程師更快地編寫程式碼。

Looking forward, we expect that the continuous advancements in Llama's coding capabilities will provide even greater leverage to our engineers and we are focused on expanding its capabilities to not only assist our engineers in writing and reviewing our code but to also begin generating code changes to automate tool updates and improve the quality of our code base.

展望未來，我們期望 Llama 編碼功能的不斷進步將為我們的工程師提供更大的優勢，我們專注於擴展其功能，不僅可以幫助我們的工程師編寫和審查我們的程式碼，還可以開始產生程式碼變更以自動化工具更新並提高我們的程式碼庫的品質。

Finally, we expect our strong financial position will enable us to support these investments while continuing to return capital to shareholders through share repurchases and dividends.

最後，我們預計強大的財務狀況將使我們能夠支持這些投資，同時繼續透過股票回購和股利向股東返還資本。

Moving to our financial outlook. We expect first quarter total revenue to be in the range of $39.5 billion to $41.8 billion. This reflects 8% to 15% year over year growth or 11% to 18% growth on a constant currency basis as our guidance assumes foreign currency as an approximately 3% headwind to year over year total revenue growth based on current exchange rates.

轉向我們的財務展望。我們預計第一季總營收將在 395 億美元至 418 億美元之間。這反映了同比增長 8% 至 15%，或按固定匯率計算的增長率 11% 至 18%，因為我們的指導假設，根據當前匯率，外幣將對同比總收入增長造成約 3% 的阻力。

This also reflects the effect of lapping leap day in the first quarter of 2024. While we are not providing a full year 2025 revenue outlook, we expect the investments we're making in our core business this year will give us an opportunity to continue delivering strong revenue growth throughout 2025.

這也反映了2024年第一季閏日疊加的影響。雖然我們沒有提供 2025 年全年的營收展望，但我們預計今年對核心業務的投資將使我們有機會在 2025 年繼續實現強勁的營收成長。

Turning now to the expense outlook. We expect full year 2025 total expenses to be in the range of $114 billion to $119 billion. We expect the single largest driver of expense growth in 2025 to be infrastructure costs driven by higher operating expenses and depreciation. We expect employee compensation to be the second largest factor as we add technical talent in the priority areas that I referenced earlier.

現在來談談費用前景。我們預計 2025 年全年總支出將在 1,140 億美元至 1,190 億美元之間。我們預計，2025 年費用成長的最大驅動力將是由於營運費用和折舊增加而導致的基礎設施成本。我們預計，隨著我們在我之前提到的優先領域增加技術人才，員工薪資將成為第二大因素。

Turning now to the CapEx outlook. We expect our full year 2025 capital expenses will be in the range of $60 billion to $65 billion. We expect CapEx growth in 2025 will be driven by increased investment to support both our generative AI efforts and our core business. The majority of our CapEx in 2025 will continue to be directed towards our core business.

現在來談談資本支出前景。我們預計 2025 年全年資本支出將在 600 億美元至 650 億美元之間。我們預計，2025 年資本支出的成長將受到增加投資的推動，以支援我們的生成性人工智慧工作和核心業務。我們 2025 年的大部分資本支出將繼續用於我們的核心業務。

On to tax. Absent any changes to our tax landscape, we expect our full year 2025 tax rate to be in the range of 12% to 15%. In addition, we continue to monitor an active regulatory landscape including legal and regulatory headwinds in the EU and the US that could significantly impact our business and our financial results.

談稅。如果我們的稅收狀況沒有任何變化，我們預計 2025 年全年稅率將在 12% 至 15% 之間。此外，我們繼續關注活躍的監管環境，包括歐盟和美國的法律和監管阻力，這些阻力可能會對我們的業務和財務表現產生重大影響。

In closing, this was a good year for our company with investments across our priority areas, delivering strong business performance and innovative new products for our community. We have a compelling set of opportunities to invest in this year which we expect will help us drive continued strong growth and develop transformative technologies that shape the future of our company and of the industry.

總而言之，對於我們公司來說，這是豐收的一年，我們在重點領域進行了投資，為我們的社區帶來了強勁的業務表現和創新的新產品。今年我們擁有一系列極具吸引力的投資機會，我們預計這些機會將幫助我們推動持續強勁成長，並開發塑造公司和產業未來的變革性技術。

With that, Krista, let's open up the call for questions.

克里斯塔，讓我們開始提問吧。

(Operator Instructions)

（操作員指示）

Brian Nowak, Morgan Stanley.

摩根士丹利的布萊恩·諾瓦克。

Mark, you sound very -- we appreciate all the excitement about this year and all the innovation to come. I know there's a lot of announcements over the course of the year. But wonder if you could just share a few sort of high level examples of your vision on new potential use cases and offerings that could drive utility for your users and value for your advertisers as you sort of think about Llama 4 and Meta AI changing throughout 2025.

馬克，你聽起來非常——我們欣賞今年的所有令人興奮的事情以及即將到來的所有創新。我知道今年會有很多公告。但是，您是否可以分享一些高層次的例子來說明您對新的潛在用例和產品的願景，這些用例和產品可以為您的用戶帶來實用性，並為您的廣告商帶來價值，因為您認為 Llama 4 和 Meta AI 將在 2025 年發生變化。

And then the second one on custom silicon. Maybe a question for either of you. Just any learnings on the difference between your custom silicon and third party chips in your ranking models and results and how should we think about the main gating factors as to how quickly you'll be able to move a higher percentage of your engagement to your custom silicon?

然後第二個是在訂製矽片上。這可能是你們任何一個人的問題。您是否了解您的排名模型和結果中客製化矽片和第三方晶片之間的差異，以及我們應該如何考慮主要的限制因素，即您能夠多快地將更高比例的參與度轉移到客製化矽片上？

On the first one, I'm -- I tried to lay this out in my opening comments a bit. I mean we're very focused on Meta AI as a highly intelligent and personalized assistant that you can access across our apps. There's a website, you can access it outside of our apps too. I think that the quality of this is just, it's going to keep on improving and improved a lot over the last year. We're also finding more ways that it's useful to integrate it into our services to help more people discover it.

關於第一個問題，我——我試著在開場白中稍微闡述這一點。我的意思是，我們非常專注於 Meta AI，它是一款高度智慧且個人化的助手，您可以透過我們的應用程式存取。有一個網站，您也可以在我們的應用程式之外訪問它。我認為它的品質將會持續改善，並且在過去的一年裡已經有很大改善。我們也正在尋找更多有用的方法將其整合到我們的服務中，以幫助更多的人發現它。

I think that that's undoubtedly why so many hundreds of millions of people are using it today is because it's kind of easy to discover what we're doing and then keep using it. I don't know. I want to keep some surprises and fun for the stuff that we're going to release this year. I gave a bit of detail on what we're planning to do with Llama 4. That I'm sure technical people will enjoy because we haven't talked about that before, but I'm going to refrain from mapping a whole lot more on what we're launching this year.

我認為毫無疑問這就是為什麼今天有數億人在使用它的原因，因為很容易發現我們在做什麼，然後繼續使用它。我不知道。我想為我們今年要發布的東西保留一些驚喜和樂趣。我詳細介紹了我們計劃如何使用 Llama 4。我相信技術人員會喜歡這個，因為我們之前沒有談論過這個，但我不會詳細談論我們今年將推出的產品。

But it's the different things that I talked about. It's Meta AI. I do expect Llama 4 to be a very exciting set of releases. It's not just one thing. Just like with Llama 3, there were kind of a few different models at different dates. I think we'll see that with Llama 4 too.

但我談論的是不同的事情。這就是元人工智慧。我確實希望 Llama 4 成為一組非常令人興奮的版本。這不只是一件事。就像 Llama 3 一樣，在不同日期有幾種不同的型號。我認為我們也會在 Llama 4 中看到這一點。

And then the AI engineer piece, I'm really excited about. I mean I don't know that that's going to be an external product anytime soon, but I think for what we're working on, our goal is to advance AI research and advance our own development internally and I think it's just going to be a very profound thing. So I mean that's something that I think will show up through making our products better over time.

然後是人工智慧工程師部分，我真的很興奮。我的意思是，我不知道這是否很快就會成為一種外部產品，但我認為，對於我們正在做的事情，我們的目標是推進人工智慧研究，推進我們自己的內部發展，我認為這將是一件非常深刻的事情。所以我的意思是，我認為隨著時間的推移，我們的產品會不斷改進，這一點將會顯現出來。

But and then as that works, there will potentially be a market opportunity down the road. But I mean for now and this year, we're really -- I think this is I don't think you're going to see this year like an AI engineer that is extremely widely deployed, changing all of development. I think this is going to be the year where that really starts to become possible and lays the groundwork for a much more dramatic change in '26 and beyond.

但隨著這項措施的實施，未來可能會出現市場機會。但我的意思是，就現在和今年而言，我們真的——我認為，我認為你不會看到今年出現部署極為廣泛的人工智慧工程師，改變整個開發過程。我認為今年這一目標將真正開始成為可能，並為 2026 年及以後更為重大的變化奠定基礎。

I don't know. Yeah, that's kind of it.

我不知道。是的，有點類似。

Brian, I'm happy to take your second question about custom silicon. So first of all, we expect that we are continuing to purchase third party silicon from leading providers in the industry. And we are certainly committed to those longstanding partnerships, but we're also very invested in developing our own custom silicon for unique workloads. Our off the shelf silicon isn't necessarily optimal.

布萊恩，我很高興回答您關於訂製矽片的第二個問題。首先，我們預計將繼續從業界領先的供應商處購買第三方矽片。我們當然致力於這些長期的合作夥伴關係，但我們也投入了大量資金來開發我們自己的用於獨特工作負載的客製化矽片。我們現成的矽片不一定是最佳的。

And specifically, because we're able to optimize the full stack to achieve greater compute efficiency and you know, performance per cost and power because our workloads might require a different mix of memory versus network, bandwidth versus compute. And so, we can optimize that really to the specific needs of our different types of workloads.

具體來說，因為我們能夠優化整個堆疊以實現更高的運算效率，並且你知道，每單位成本和功率的效能，因為我們的工作負載可能需要不同的記憶體與網路、頻寬與運算的組合。因此，我們可以根據不同類型工作負載的特定需求進行最佳化。

Right now, the inhouse MTIA program is focused on supporting our core ranking and recommendation inference workloads. We started adopting MTIA in the first half of 2024 for core ranking and recommendations [inferences].

目前，內部 MTIA 計劃專注於支持我們的核心排名和建議推理工作負載。我們從 2024 年上半年開始採用 MTIA 進行核心排名和推薦[推論]。

We'll continue ramping adoption for those workloads over the course of 2025 as we use it for both incremental capacity and to replace some GPU based servers when they reach the end of their useful lives. You know, next year, we're hoping to expand MTIA to support some of our core AI training workloads and over time some of our Gen AI use cases.

在 2025 年期間，我們將繼續增加這些工作負載的採用，因為我們既可以將其用於增加容量，也可以在某些基於 GPU 的伺服器達到使用壽命時將其替換掉。您知道，明年，我們希望擴展 MTIA 以支援我們的一些核心 AI 訓練工作負載，並隨著時間的推移支援我們的一些 Gen AI 用例。

Eric Sheridan, Goldman Sachs.

高盛的艾瑞克·謝裡丹。

Thank you so much for taking the question. Maybe I can go back to your comments on open source. Can you help us understand how your views continue to evolve with respect to the competitive dynamic around your approach with open source versus others in the industry and how your approach to open source could possibly bend the cost curve and improve return on capital for AI over the medium to long term. Thanks so much.

非常感謝您回答這個問題。也許我可以回到你對開源的評論。您能否幫助我們了解，您對開源方法與業內其他方法的競爭動態的看法是如何不斷演變的，以及您的開源方法如何在中長期內改變成本曲線並提高人工智慧的資本回報率。非常感謝。

Yeah, I mean, on open source, I think the best analogy for us is what we did with open compute where we -- we weren't first to building the system. So then by the time that we got around to building it wasn't really a big advantage to have it be proprietary. So we shared it and then a lot of the industry adopted what we were doing, contributed innovations back to it by standardizing it -- on it.

是的，我的意思是，關於開源，我認為對我們來說最好的類比就是我們對開放運算所做的事情——我們並不是第一個建立這個系統的人。因此，當我們開始建造時，將其作為專有技術實際上並沒有太大的優勢。因此，我們分享了它，然後許多行業都採用了我們的做法，並透過標準化為其貢獻了創新。

That meant that a bunch of supply chains standardized on building it, which made prices more efficient for everyone. You know, I think what we see here is as Llama becomes more used, it's more likely for example that silicon providers and others -- other APIs and developer platforms will optimize their work more for that. And basically, drive down the costs of using it and drive improvements that we can in some cases use too. So I think that the strategy will continue to be effective.

這意味著大量供應鏈在其構建上實現標準化，從而使每個人的價格更加有效。你知道，我認為我們在這裡看到的是，隨著 Llama 的使用越來越廣泛，例如矽片提供者和其他方——其他 API 和開發者平台將更有可能為此優化他們的工作。基本上，降低使用它的成本並推動我們在某些​​情況下也可以使用的改進。所以我認為該策略將繼續有效。

And yeah, I mean, I continue to be optimistic about this. I think it's working. I also -- I just think, you know, in light of some of the recent news, you know, the new competitor, DeepSeek from China, I think, it also just puts -- it's one of the things that we're talking about is there's going to be an open source standard globally.

是的，我的意思是，我對此仍然持樂觀態度。我認為它正在發揮作用。我還——我只是認為，你知道，鑑於最近的一些新聞，你知道，新的競爭對手，來自中國的 DeepSeek，我認為，它也只是——我們正在談論的事情之一是全球將會有一個開源標準。

And I think for our kind of a national advantage, it's important that it's an American standard. So we take that seriously and we want to build the AI system that people around the world are using. And I think that if anything, some of the recent news has only strengthened our conviction that this is the right thing for us to be focused on.

我認為，對於我們的國家優勢而言，美國標準非常重要。因此，我們認真對待這個問題，並希望建立全世界的人們都在使用的人工智慧系統。我認為，最近的一些新聞只會加強我們的信念，讓我們相信這是我們應該關注的正確的事情。

Mark Shmulik, Bernstein.

馬克‧施穆里克，伯恩斯坦。

Yes, thank you for taking my questions. Mark, appreciate we may get an answer this year but looking out, as you kind of track the progress of smart glasses, you know, Orion and so forth, do you view that as a better form factor to get the most out of the Meta AI assistance you highlighted in your opening remarks? Or is it more complementary to kind of the in-app experience and the way you've seen people use it today?

是的，感謝您回答我的問題。馬克，我們可能會在今年得到答案，但從長遠來看，當你追蹤智慧眼鏡（你知道，Orion 等）的進展時，你是否認為這是一種更好的形式，可以最大限度地利用你在開場白中強調的 Meta AI 輔助功能？或者它是否對應用程式內體驗以及您今天看到的人們使用它的方式更具補充性？

And then Susan, the last few quarters, we've kind of seen pricing growth as the dominant driver of ad revenue growth. Given the efforts you've highlighted around driving deeper, more commercial engagement and better advertiser ROI, how do we just think about the contribution or the formula for ad revenue growth going forward? Thank you.

蘇珊，過去幾個季度，我們已經看到價格成長成為廣告收入成長的主要驅動力。鑑於您所強調的推動更深入、更多商業參與和更好的廣告商投資回報率的努力，我們如何看待未來廣告收入成長的貢獻或公式？謝謝。

Yeah, I mean, I can talk about glasses. I mean, it's a -- yeah and I've said for a while that I think that glasses are the ideal form factor for an AI device because you can let an AI assistant on your glasses see what you see and hear what you hear, which gives it the context to be able to understand everything that's going on in your life that you would want to talk to it about and get context on.

是的，我的意思是，我可以談論眼鏡。我的意思是，是的，我說過一段時間，我認為眼鏡是人工智慧設備的理想外形，因為你可以讓眼鏡上的人工智慧助理看到你所看到的，聽到你所聽到的，這為它提供了背景，使它能夠理解你生活中發生的一切，你想與它談論並了解背景。

So -- but look, I mean, I think the glasses are going to be a very important computing platform in the future. when phones became the primary computing platform, it's not like computers went away. You know, I think we'll have phones for some time, but there are a lot of people in the world who have glasses, it's kind of hard for me to imagine that a decade or more from now, all the glasses aren't going to basically be AI glasses, as well as a lot of people who don't wear glasses today finding that to be a useful thing.

所以——但是看，我的意思是，我認為眼鏡將來會成為一個非常重要的計算平台。當手機成為主要的運算平台時，並不代表電腦就消失了。你知道，我認為我們會在一段時間內擁有手機，但世界上有很多人戴著眼鏡，我很難想像十年或更長時間後，所有的眼鏡基本上不會是人工智能眼鏡，而且很多今天不戴眼鏡的人會發現它很有用。

So I'm incredibly optimistic about this. And like I shared last year, I think one of the big surprises last year was I previously thought that glasses weren't going to become a major form factor until we got these -- the full kind of holographic displays that we started showing in the prototype for Orion.

所以我對此非常樂觀。正如我去年分享的那樣，我認為去年最大的驚喜之一是，我之前認為眼鏡不會成為一種主要的形式因素，直到我們得到這些——我們在 Orion 原型中開始展示的全息顯示器。

But now I think it's pretty clear that AI is actually going to drive at least as much of the value as the holographic AR is. So that's a cause to be excited. But look the Ray-Ban Metas were hit. We still don't know what the long-term trajectory for this is going to be. And I think we're going to learn a lot this year. So I think that this is a really important year for that.

但現在我認為很明顯，人工智慧實際上將至少發揮與全息 AR 同等重要的價值。這是令人興奮的事。但看看雷朋 Metas 受到了打擊。我們仍然不知道這件事的長期發展軌跡將會是怎樣。我認為我們今年將會學到很多東西。所以我認為今年是非常重要一年。

And I can take the second question pricing growth. So first of all, what I would say is over the long term, we think we have continued opportunity to drive revenue growth across both pricing and impression growth. So both sort of supply and demand dimensions.

我可以回答第二個問題，即定價成長。首先，我想說的是，從長遠來看，我們認為我們有機會繼續透過定價和印象成長來推動收入成長。因此，既有供給維度，也有需求維度。

When we look at pricing, our reported growth can be influenced by different factors such as supply because of the option dynamics by the mix shift of the different types of services where ads show up, for example, services like video or lower monetization efficiency, relatively speaking, and then of course, you know, broader macro factors.

當我們考慮定價時，我們報告的成長可能會受到不同因素的影響，例如供應，因為不同類型的服務的組合變化導致選項動態，廣告會出現在這些服務中，例如影片或相對而言較低的貨幣化效率等服務，當然，還有更廣泛的宏觀因素。

But we generally expect that, we are going to be able to deliver ongoing ad performance improvements through a lot of the ongoing work that we're doing across our monetization road map. And that will have the sort of effect of benefiting pricing overall.

但我們總體上預計，我們將能夠透過我們在貨幣化路線圖上正在進行的大量工作來持續提高廣告成效。這將對整體定價產生一定的影響。

And part of, what I think is kind of important to think about here when we think about price growth is we really -- the average price per ad as we report it is really blending, it's an output metric. It's blending a lot of things that are happening including what are advertisers bidding for, what are their bids for those things, what is the average cost of their actions?

當我們考慮價格成長時，我認為需要考慮的重要一點是——我們報告的每個廣告的平均價格實際上是混合的，它是一個產出指標。它融合了很多正在發生的事情，包括廣告商競標什麼，他們對這些東西的出價是多少，他們行動的平均成本是多少？

So given that there are so many different objectives that advertisers can optimize for that have different values. It's a very complex metric that tries to distill that into one thing. Overall, we are seeing healthy cost per action trends for advertisers for whatever is the action that they are optimizing for. And we believe we'll continue to get better at driving conversions for advertisers. And when we do that will have the effect of continuing to lift CPMs over time because we're delivering more conversions per impressions served resulting in higher value impressions.

因此，鑑於廣告商可以優化許多具有不同價值的目標。這是一個非常複雜的指標，試圖將其提煉為一個事物。整體而言，我們看到廣告主無論優化什麼行動，每次行動成本趨勢都比較健康。我們相信，我們將繼續更好地推動廣告商的轉換。當我們這樣做時，隨著時間的推移，每千次曝光費用 (CPM) 將會持續上升，因為我們為每次展示提供了更多的轉化，從而帶來了更高價值的展示。

Justin Post, Bank of America.

美國銀行的賈斯汀·波斯特 (Justin Post)。

Great. Thanks, maybe one for Mark and one for Susan. Mark, you mentioned political changes in the US and better positioning maybe for us companies abroad. But how do you think about it in the US as far as usage and advertiser adoption?

偉大的。謝謝，也許一個給馬克，一個給蘇珊。馬克，您提到了美國的政治變化，這也許會為我們在海外的公司帶來更好的定位。但就其在美國的使用情況和廣告商採用情況而言，您如何看待它？

You got rid of fact checking. So do you think your content could change? Could it appeal to more users? Will that impact advertising at all? And then Susan on Meta AI, I know people are pretty excited about the use case, but also thinking about the revenue case, how do you think about monetizing that, could it be CPC ads or how are you thinking about that? Thank you.

您已經擺脫了事實查核。那麼您認為您的內容會改變嗎？它能吸引更多用戶嗎？這會對廣告產生影響嗎？然後 Susan 談到 Meta AI，我知道人們對用例感到非常興奮，但也考慮收入情況，您如何考慮將其貨幣化，可能是 CPC 廣告還是您對此有何看法？謝謝。

The question was about fact checking in our content policies. I mean, look, I think we're trying to build the service that we think is the best for people. You know, I believed in free expression for quite a while. People don't want to see misinformation, but you need to build an effective system that gives people more context. And I think what we found over time is that the community note system, I think is just going to be more effective than the system that we had before.

問題是關於我們內容政策中的事實查核。我的意思是，看，我認為我們正在努力建立我們認為對人們最好的服務。你知道，我很久以來都相信言論自由。人們不想看到錯誤的訊息，但你需要建立一個有效的系統，為人們提供更多背景資訊。我認為，隨著時間的推移，我們發現社區筆記系統將比我們以前的系統更有效。

And I'm not afraid to admit when someone does something that's better than us. I think it's sort of our job to go and just do the best work and implement the best system. So I think that there's been a lot of people who have read this announcement as if, we somehow don't care about adding context to things that are on our platform that are misinformation. That's not right. I actually think that the community notes system, like what X has had for a while is actually just more effective than what we were doing before. And I think our product is going to get better because of it.

當有人做的事情比我們更好時，我並不害怕承認。我認為我們的工作就是盡力做好工作並實施最好的系統。所以我認為很多人讀過這個聲明後，似乎覺得我們並不關心為我們平台上的錯誤訊息添加背景資訊。那是不對的。我實際上認為社群筆記系統（就像 X 一段時間以來所採用的系統）實際上比我們以前所做的更有效。我認為我們的產品將會因此變得更好。

I would add to that just to say we also haven't seen any noticeable impact from our content policy changes on advertiser spend. So we're continuing to see strong advertiser demand, again, particularly for AI powered tools that are helping businesses maximize the value of their ad spend. So our commitment to brand safety is unchanged and we expect that we will invest in our suite of tools to meet the needs of advertisers.

我想補充一點，我們還沒有看到內容政策變化對廣告商支出有任何明顯的影響。因此，我們持續看到強勁的廣告主需求，特別是對於幫助企業最大化廣告支出價值的人工智慧工具。因此，我們對品牌安全的承諾沒有改變，我們希望投資我們的工具套件來滿足廣告商的需求。

On your second question in terms of monetizing Meta AI, our initial focus for Meta AI is really about building a great consumer experience. And that's frankly where all of our energies are kind of directed to right now. There will I think be pretty clear monetization opportunities through over time including paid recommendations and including a premium offering. But that's, really not where we are focused in terms of the development of Meta AI today.

關於您關於 Meta AI 貨幣化的第二個問題，我們對 Meta AI 的最初關注點實際上是打造出色的消費者體驗。坦白說，這就是我們現在所有精力所集中的方向。我認為隨著時間的推移，將會有相當明顯的獲利機會，包括付費推薦和優質產品。但就現今的元人工智慧發展而言，這並不是我們真正關注的重點。

Douglas Anmuth, JPMorgan.

摩根大通的道格拉斯·安穆斯。

Thanks for taking the questions. One for Mark, one for Susan. Mark just following up on open source as DeepSeek and other models potentially leverage Llama or others to train faster and cheaper. How does this impact Meta in your view? And what could it mean for the trajectory of investment required over a multi-year period?

感謝您回答這些問題。一個給馬克，一個給蘇珊。Mark 只是在跟進開源，因為 DeepSeek 和其他模型可能會利用 Llama 或其他模型來更快、更便宜地進行訓練。您認為這對 Meta 有何影響？這對於多年所需的投資軌跡意味著什麼？

And then Susan, just as we think about the $60 billion to $65 billion in CapEx this year, does the composition change much from last year when you talked about servers as the largest part followed by data centers and networking equipment? And how should we think about that mix between like training and inference just following up on Jon's post this week? Thanks.

然後蘇珊，正如我們考慮今年的 600 億至 650 億美元資本支出時所言，其組成與去年相比是否有很大變化？去年您談到伺服器是最大的組成部分，其次是資料中心和網路設備？那麼，根據喬恩本週的帖子，我們應該如何看待訓練和推理之間的結合？謝謝。

I can start on the DeepSeek question. You know, I think that there's a number of novel things that they did that I think we're still digesting and there are a number of things that they have advances that we will hope to implement in our systems. And that's part of the nature of how this works, whether it's a Chinese competitor or not. You know, I kind of expect that every new company that has an advance, that has a launch, is going to have some new advances that the rest of the field learns from and that's sort of how the technology industry goes.

我可以開始討論 DeepSeek 問題。你知道，我認為他們做了許多新穎的事情，我認為我們仍在消化這些事情，他們也取得了許多進步，我們希望在我們的系統中實現這些進步。這就是事情的本質，無論是否有中國競爭對手。你知道，我希望每一家取得進步、推出新產品的新公司都會取得一些新的進步，讓其他領域的人可以藉鑑，這就是科技業的發展方式。

I don't know, it's probably too early to really have a strong opinion on what this means for the trajectory around infrastructure and CapEx and things like that. You know, there are a bunch of trends that are happening here all at once.

我不知道，現在就對這對基礎設施、資本支出等的發展軌跡意味著什麼下結論可能還為時過早。你知道，這裡同時出現了許多趨勢。

There's already sort of a debate around how much of the compute infrastructure that we're using is going to go towards pre-training versus as you get more of these reasoning time models or reasoning models where you get more of the intelligence by putting more of the compute into inference, whether just it will mix shift how we use our compute infrastructure towards that.

目前已經存在一種爭論，關於我們使用的計算基礎設施中有多少將用於預訓練，還是隨著你獲得更多的推理時間模型或推理模型，通過將更多的計算投入推理來獲得更多的智能，它是否會混合轉變我們使用計算基礎設施的方式。

That was already something that I think a lot of the other labs and ourselves we're starting to think more about. And already seemed pretty likely even before this that like of all the compute that we're using that, you know, the largest pieces aren't necessarily going to go towards pre-training, but that doesn't mean that you need less compute. Because one of the new properties that's emerged is the ability to apply more compute at inference time in order to generate a higher level of intelligence, a higher quality of service, which means that as a company that, you know, has a strong business model to support this.

我認為很多其他實驗室和我們自己已經開始更多地思考這個問題。甚至在此之前就已經很有可能，在我們使用的所有計算中，最大的部分不一定會用於預訓練，但這並不意味著你需要更少的計算。因為出現的新功能之一是能夠在推理時應用更多的計算，以產生更高水平的智能、更高質量的服務，這意味著作為一家公司，你知道，它擁有強大的商業模式來支持這一點。

I think that's generally an advantage that we're now going to be able to provide a higher quality of service than others who don't necessarily have the business model to support it on a sustainable basis. The other thing is just that, when we're building things like Meta AI, but also how we're implementing AI, all the feeds and Ad products and things like that, we're just serving billions of people, which is different from, okay, you start to pre-train a model and that model is sort of agnostic to how many people are using it.

我認為這通常是一個優勢，我們現在能夠提供比其他不一定擁有可持續支援業務模式的公司更高品質的服務。另一件事是，當我們建立像 Meta AI 這樣的東西時，以及我們如何實現 AI、所有資訊流和廣告產品等等時，我們只是在為數十億人提供服務，這不同於你開始預先訓練一個模型，而這個模型對於有多少人在使用它是不可知的。

Like at some level, it's going to be expensive for us to serve all of these people because we are serving a lot of people. And so I'm not sure what the kind of net effect of all of this is.

就像在某種程度上，為所有這些人提供服務的成本會很高，因為我們要服務很多人。所以我不確定這一切的最終效果是什麼。

The field continues to move quickly. There's a lot to learn from releases from basically everyone who does something interesting, not just this -- not just the ones over the last month. We'll continue to kind of incorporate that into what we do as well as making novel contributions to the field ourselves. And I continue to think that investing very heavily in CapEx and infra is going to be a strategic advantage over time.

該領域繼續快速發展。基本上，每個做有趣事情的人所發布的消息都有很多值得學習的地方，不僅僅是這個——不僅僅是上個月的那些。我們將繼續將其融入我們的工作中，並為該領域做出新的貢獻。我仍然認為，從長遠來看，大力投資資本支出和基礎設施將成為一種策略優勢。

It's possible that we'll learn otherwise at some point. But I just think it's way too early to call that. And at this point, I would bet that the ability to build out that kind of infrastructure is going to be a major advantage for both the quality of the service and being able to serve the scale that we want to.

我們有可能在某個時候學到其他東西。但我認為現在下結論還為時過早。此時，我敢打賭，建造這種基礎設施的能力對於服務品質和達到我們想要的服務規模都將是一個重大優勢。

I'm happy to add a little more color about our 2025 CapEx plans to your second question. So we certainly expect that 2025 CapEx is going to grow across all three of those components you described. Servers will be the biggest growth driver that remains the largest portion of our overall CapEx budget. We expect both growth in AI capacity as we support our Gen A I efforts and continue to invest meaningfully in core AI.

我很高興為您的第二個問題補充一些有關我們 2025 年資本支出計劃的詳細資訊。因此，我們當然預計 2025 年的資本支出將在您所描述的所有三個組成部分中成長。伺服器將成為最大的成長動力，並仍占我們整體資本支出預算的最大份額。隨著我們支持 Gen A I 工作並繼續對核心 AI 進行有意義的投資，我們預計 AI 容量將會成長。

But we are also expecting growth in non-AI capacity as we invest in the core business, including to support a higher base of engagement and to refresh our existing servers. On the data center side, we're anticipating higher Data center spend in 2025 to be driven by buildouts of our large training lusters and our higher power density data centers that are entering the core construction phase.

但隨著我們對核心業務的投資，包括支援更高的參與基礎和更新我們現有的伺服器，我們也預期非人工智慧容量也會成長。在資料中心方面，我們預計 2025 年資料中心支出將增加，這得益於我們大型培訓中心的建設以及正在進入核心建設階段的高功率密度資料中心的建設。

We're expecting to use that capacity primarily for core AI and non-AI use cases. On the networking side, we expect networking spend to grow in '25 as we build higher capacity networks to accommodate the growth in non-AI and core AI related traffic along with our large Gen AI training clusters. We're also investing in fiber to handle future cross-region training traffic.

我們希望將該容量主要用於核心 AI 和非 AI 用例。在網路方面，我們預計 25 年網路支出將會成長，因為我們將建立更高容量的網路來適應非人工智慧和核心人工智慧相關流量的成長以及我們大型的 Gen AI 訓練集群。我們也投資光纖來處理未來的跨區域訓練流量。

And then in terms of the breakdown for core versus Gen AI use cases, we're expecting total infrastructure spend within each of gen AI, non-AI, and core AI to increase in '25 with the majority of our CapEx directed to our core business with some caveat that's not easy to measure perfectly as the data centers we're building can support AI or non-AI workloads and the GPU based servers we procure for Gen AI can be repurposed for core AI use cases and so on and so forth.

然後，就核心與通用人工智慧用例的細分而言，我們預計 25 年通用人工智慧、非人工智慧和核心人工智慧的總基礎設施支出將會增加，我們的大部分資本支出將用於核心業務，但有一些需要注意的事項，這些事項並不容易完美衡量，因為我們正在建構的資料中心可以支援人工智慧或非人工智慧工作負載，我們為通用人工智慧採購的基於 GPU 人工智慧的

But overall, I would reiterate what Mark said, we are committed to building leading foundation models and applications. We expect that we're going to make big investments to support our training and inference objectives, and we don't know exactly where we are in the cycle of that, yeah.

但總的來說，我想重申馬克所說的話，我們致力於建立領先的基礎模型和應用程式。我們預計將進行大規模投資來支持我們的培訓和推理目標，但我們並不確切知道我們處於這個週期的哪個階段，是的。

Ron Josey, Citigroup.

花旗集團的 Ron Josey。

Hey, thanks for taking the question. Mark, I want to get back to your comment on getting back to the OG Facebook and I want to understand a little bit more on the use cases and how that could expand. Video is clearly a benefit. Local marketplace groups have all been positive. So any insights on the OG Facebook. And then back to Meta AI, given the adoption we're seeing on the 600-plus MAUs. Just how does the user experience evolved here? What are people doing with Meta AI? Thank you.

嘿，謝謝你回答這個問題。馬克，我想回到你關於回到 OG Facebook 的評論，我想更多地了解用例以及如何擴展。視頻顯然是一個優勢。當地市場團體都持正面態度。那麼對 OG Facebook 有什麼見解嗎？然後回到 Meta AI，鑑於我們看到的 600 多個 MAU 的採用情況。使用者體驗在這裡究竟是如何演變的？人們用 Meta AI 做什麼？謝謝。

Okay. So for Facebook, a lot of people use Facebook every day and it's an important part of their lives. And I think that there are a lot of opportunities to make it way more culturally influential than it is today. And I think that that's sort of a fun and interesting goal that will take our product development and in some interesting directions that we maybe haven't focused on as much over the last several years.

好的。對 Facebook 來說，很多人每天都使用 Facebook，它是他們生活中的重要部分。我認為有很多機會可以讓它比現在更具文化影響力。我認為這是一個有趣的目標，它將引領我們的產品開發朝著一些有趣的方向發展，而過去幾年我們可能沒有太多關注這些方向。

So, I don't know that I have anything much more specific on this other than that, this is going to be a one of my focus areas for this year. I think it's an investment area and something I'm going to spend some time on it. It might mean that in the near term we make some, some trade-offs to kind of focus on some product areas of what we're doing ahead of just kind of maximizing business results in the near term on it.

所以，除了這一點之外，我不知道我還有沒有什麼更具體的事情要做，這將是我今年的重點領域之一。我認為這是一個投資領域，我會花一些時間在上面。這可能意味著，在短期內，我們會做出一些權衡，將注意力集中在我們正在做的某些產品領域，而不是在短期內最大化業務成果。

But overall, I'm really excited about doing some exciting stuff here and not going to get into many specifics now. But we'll follow up on this over the next, I call it half a year, a year as we start rolling stuff out. And I think some of this will kind of get back to how Facebook was originally used back in the day. So I think it'll be fun.

但總的來說，我真的很高興在這裡做一些令人興奮的事情，現在我不想談論太多細節。但我們會在接下來的半年或一年內跟進此事，因為我們會開始推出新產品。我認為其中一些將會回歸 Facebook 最初使用的方式。所以我認為這會很有趣。

I'm happy to share a little bit more about Meta AI and what people are doing with that. We are in a phase where we are really learning a lot from the way that people engage with Meta AI. So, from an app perspective, WhatsApp continues to see the strongest Meta AI usage across our family of apps. People there are using it most frequently for information seeking and educational queries along with emotional support use cases. Most of the WhatsApp engagement is in one-on-one threads that we see some usage In group messaging.

我很高興與大家分享更多關於 Meta AI 以及人們正在如何使用它的資訊。我們正處於一個從人們與元人工智慧互動的方式中學習很多東西的階段。因此，從應用程式的角度來看，WhatsApp 在我們的應用程式系列中繼續擁有最強大的 Meta AI 使用率。那裡的人們最常使用它來尋求資訊和進行教育查詢以及提供情感支持。WhatsApp 的大部分互動都是一對一的，我們也看到它在群發訊息中也有一些使用。

And on Facebook, you know, which is the second largest driver of Meta AI engagement, we're seeing strong engagement from our feed deep dives integration that lets people ask Meta AI questions about the content that is recommended to them.

在 Facebook 上，您知道，這是 Meta AI 參與的第二大驅動力，我們看到我們的 feed 深度挖掘整合帶來了強大的參與度，它讓人們可以向 Meta AI 詢問有關推薦給他們的內容的問題。

So across, I would say all query types, we continue to see signs that Meta AI is helping people leverage our apps for new use cases. You know, we talked about information gathering, social interaction, and communication. Lots of people use it for humor and casual conversation. They use it for writing and editing research recommendations.

因此，我想說，在所有查詢類型中，我們不斷看到跡象表明 Meta AI 正在幫助人們利用我們的應用程式來實現新的用例。你知道，我們談論了資訊收集、社交互動和溝通。許多人用它來幽默和隨意交談。他們用它來撰寫和編輯研究建議。

And as we look forward to 2025 in our Meta AI roadmap, we are really focused on doing more to make it feel more personalized. So I would say some of the most exciting features we're working on including improving sort of the memory dimension of the Meta AI experience where it will be able to remember certain details that people share in one-on-one chats, for example, and use those details to personalize its responses and then really increasing its ability to deliver great content recommendations and enhance really what makes Facebook and Instagram so valuable for people today.

當我們在 Meta AI 路線圖中展望 2025 年時，我們真正專注於做更多的事情以使其更加個性化。因此，我想說，我們正在開發的一些最令人興奮的功能包括改進 Meta AI 體驗的記憶維度，例如，它將能夠記住人們在一對一聊天中分享的某些細節，並使用這些細節來個性化其回复，然後真正提高其提供優質內容推薦的能力，並真正增強 Facebook 和 Instagram 對當今人們的價值。

Ken Gawrelski, Wells Fargo.

富國銀行的肯‧加夫雷爾斯基 (Ken Gawrelski)。

Thank you very much. Two for me, please. First, could you talk a little bit -- I know you talked a little bit on the capital intensity side and the recent developments, and it's hard to see -- it's hard to tell yet where things are going. But maybe you could just talk a little bit more near term, '25, the CapEx budget you laid out or the CapEx forecast. Could you talk a little bit about the constraints you're seeing or where you're seeing constraints, either internally resources planning or externally and anyone -- any parts of the ecosystem.

非常感謝。請給我兩份。首先，您能否談一談——我知道您談了一些資本密集度方面和最近的發展情況，但目前還很難看出——很難判斷事情將如何發展。但也許您可以多談談近期的情況，即您列出的 25 年資本支出預算或資本支出預測。您能否談談您所看到的限製或您看到限制的地方，無論是內部資源規劃還是外部以及任何人 - 生態系統的任何部分。

And then on the second one, I'm curious, as you think about the -- as you think about your needs for hiring and we just think about -- we know you gave the OpEx guide for this year. But as we think about future needs for hiring, could you just give us a sense of how we should think about that? You announced the performance-related reductions earlier this -- for early this year. Could you just talk about how we should be thinking about that '26, '27 and beyond? Thank you.

然後關於第二個問題，我很好奇，當您考慮 - 當您考慮您的招聘需求時，我們只是考慮 - 我們知道您給出了今年的營運支出指南。但是，當我們考慮未來的招募需求時，您能否告訴我們應該如何考慮這個問題？您在今年稍早宣布了與績效相關的削減——將於今年年初實施。您能否談談我們應該如何看待 26 年、27 年及以後的事？謝謝。

Sure. I'm happy to take both of those. So on your first question on just where do we see constraints in our ability to execute against our CapEx plans, obviously, we are staying on top of supply availability. That is certainly one of the factors that will influence our CapEx spend in 2025, but we don't really have any updates to share on supply availability right now.

當然。我很樂意接受這兩個。因此，關於您的第一個問題，即我們在哪些方面限制了我們執行資本支出計劃的能力，顯然，我們正在密切關注供應的可用性。這肯定是影響我們 2025 年資本支出的因素之一，但目前我們還沒有任何有關供應可用性的最新消息可以分享。

We are planning to significantly ramp up deployment of GPUs in 2025, and we'll continue to engage with our vendors and invest in our own silicon to meet those needs. When you asked how to think about capital intensity, we're not really -- as both Mark and I alluded to in our prior comments, I think it is really too early to determine what long-run capital intensity is going to look like. There are so many different factors.

我們計劃在 2025 年大幅增加 GPU 的部署，並且我們將繼續與供應商合作並投資於我們自己的矽片以滿足這些需求。當你問到如何看待資本密集度時，我們實際上並沒有——正如馬克和我在我們之前的評論中提到的那樣，我認為現在確定長期資本密集度將會是什麼樣子還為時過早。有很多不同的因素。

The pace of advancement in underlying models, how efficient can they be? What is the adoption and use case of our Gen AI products, what performance gains come from next-generation hardware innovations, both our own and third party and then ultimately, what monetization or other efficiency gains our AI investments unlock. So again, I think we are -- we're sort of early in the journey here, and we don't have -- I would say we don't have kind of anything to share about long-run capital intensity yet.

底層模型的進步速度，效率能有多高？我們的 Gen AI 產品的採用和使用情況是什麼，下一代硬體創新（包括我們自己的和第三方的）帶來了哪些性能提升，最終，我們的 AI 投資將帶來哪些貨幣化或其他效率提升。所以，我再說一遍，我們認為我們 - 我們還處於起步階段，我們還沒有 - 我想說我們還沒有任何關於長期資本密集度的信息可以分享。

Your second question was about thinking about hiring needs. So it's a good segue after infrastructure, employee compensation is the next largest driver of expense growth in 2025. And here, growth in employee comp and headcount more broadly is primarily driven by those areas that I mentioned, infrastructure monetization, generative AI, Reality Labs and regulation and compliance. And those generally are more technical organizations.

您的第二個問題是關於招募需求的思考。因此，繼基礎設施之後，員工薪酬將成為 2025 年支出成長的下一個最大驅動力。在這裡，員工薪酬和員工數量的成長主要受到我所提到的那些領域的推動，即基礎設施貨幣化、生成人工智慧、現實實驗室以及監管和合規。這些組織通常都是技術性較強的組織。

That means that it is a higher cost base relative to business functions where we are also expecting to keep headcount growth constrained. And I would say we are -- we're focused on running the company efficiently. But at the same time, it is -- we feel like we're in a critical period in terms of making sure that we are investing to win, and we want to make sure that we staff those priority areas in a way that really positions us to best do that.

這意味著相對於業務功能而言，其成本基礎較高，我們也希望限制員工人數的成長。我想說的是，我們專注於高效經營公司。但同時，我們感覺我們正處於一個關鍵時期，需要確保我們進行投資以贏得勝利，我們希望確保我們在這些優先領域配備人員，以便我們能夠最好地做到這一點。

Krista, we have time for one last question.

克里斯塔，我們還有時間問最後一個問題。

Ross Sandler, Barclays.

巴克萊銀行的羅斯桑德勒。

Yeah. One for Mark, on agents. So we all saw OpenAI's Operator demo last week. So Mark, as the industry moves from chat to agenetic behavior and more commercial intent moves into these AI products. I guess how are you thinking about monetization potential for Meta AI? And then how might Llama 4 reasoning help drive some of these new genetic experiences for Meta AI? Thank you.

是的。一個是給馬克的，關於特務。所以我們上週都看到了 OpenAI 的 Operator 示範。所以馬克，隨著產業從聊天轉向基因行為，更多的商業意圖轉移到這些人工智慧產品。我猜您如何看待 Meta AI 的貨幣化潛力？那麼 Llama 4 推理如何幫助推動 Meta AI 的一些新的遺傳體驗呢？謝謝。

Yeah. So I guess a couple of things that I'd say on this. One is when you're thinking about agents and reasoning, a lot of this is about being able to perform multistep tasks. So right now, the way that a lot of these systems work as you kind of say something and then it responds and it's almost chat like. But I think that the direction that it's going is you're going to be able to give it an intent or a task and it's going to be able to go off and use sort of an arbitrary amount of compute as much as you want to use on it to be able to do a task.

是的。所以我想我會就此說幾件事。一方面，當你考慮代理和推理時，很多都是關於能夠執行多步驟任務。所以現在，許多這些系統的工作方式是，你只要說一句話，它就會做出反應，幾乎就像聊天一樣。但我認為它的發展方向是，你將能夠賦予它一個意圖或一項任務，它將能夠使用任意數量的計算，只要你想用它就可以完成一項任務。

Some of the tasks might be pretty simple for people go buy a specific thing. Some of them might be really hard, like go write an app or optimize this code and like really make it as good as possible. And that type of thing, I think, is just going to start becoming more and more prevalent over the next a year or two.

對於購買特定物品的人來說，某些任務可能非常簡單。其中一些可能真的很難，例如編寫一個應用程式或優化這段程式碼並使其盡可能好。我認為，這種事情在未來一兩年內會變得越來越普遍。

So I think it's very exciting. It's sort of will feel in some ways like the current products are just getting smarter and others, it will feel like sort of a new form factor because it won't be as much like chat. But it's sort of another generation of these products.

所以我認為這非常令人興奮。在某些方面，它感覺就像當前的產品變得越來越智能，而在其他方面，它感覺像是一種新的形式因素，因為它不太像聊天。但它是這些產品的另一代產品。

So I think it's just in general, there's a lot to build and be excited about. I guess my note of caution or just my kind of periodic reminder on our product development process, if you will, is we build these products. We try to scale them to reach usually 1 billion people or more. And it's at that point once they're at scale that we really start focusing on monetization.

所以我認為總的來說，有很多事情值得建設和興奮。我想我的警告或只是我們對我們的產品開發過程的一種定期提醒，如果你願意的話，就是我們製造這些產品。我們嘗試將其規模擴大到通常覆蓋 10 億人或更多。一旦它們達到規模，我們就會真正開始關注貨幣化。

So sometimes we'll experiment with monetization before -- we're running some experiments with Threads now, for example. But we typically don't really ramp these things up or see them as meaningfully contributing to the business until we reach quite a big scale. So the thing that I think is going to be meaningful this year is the kind of getting of the AI product to scale.

因此，有時我們會嘗試貨幣化——例如，我們現在正在對 Threads 進行一些實驗。但我們通常不會真正加大這些投入，也不會認為它們對業務有實質的貢獻，除非我們的規模達到相當大的程度。所以我認為今年有意義的事情是人工智慧產品的規模化。

Last year was sort of the introduction and starting to get to be used. This year my kind of expectation and hope is that we will be at a sufficient scale and have sufficient kind of flywheel of people using it and improvement from that, that this will have a durable advantage. But that doesn't mean that it's going to be a major contributor to the business this year.

去年是介紹階段，開始投入使用。今年我的期望和希望是，我們將達到足夠的規模，並有足夠多的人使用它，並在此基礎上進行改進，這將具有持久的優勢。但這並不意味著它將成為今年業務的主要貢獻者。

This year the improvements of the business are going to be taking the AI methods and applying them to advertising and recommendations and feeds and things like that. So the actual business opportunity for Meta AI and AI Studio and business agents and people interacting with these AIs remains outside of '25 for the most part.

今年，業務的改進將採用人工智慧方法並將其應用於廣告、推薦、資訊流等方面。因此，Meta AI 和 AI Studio 以及業務代理和與這些 AI 互動的人員的實際商業機會大部分仍在 25 年之外。

And I think that's an important thing for us to communicate and for people to internalize as you're thinking about our prospects here. But nonetheless, we've run a process like this many times. We built a product. We make it good. We scale it to be large. We build out the business around it. That's what we do. I'm very optimistic, but it's going to take some time.

我認為，當您考慮我們的前景時，這對我們進行溝通和讓人們內化是一件重要的事情。但儘管如此，我們已經多次運行類似的流程。我們製造了一個產品。我們做得非常好。我們將其縮放到很大。我們圍繞著它開展業務。這就是我們所做的。我非常樂觀，但這需要一些時間。

Thank you everyone for joining us today. We appreciate your time, and we look forward to speaking with you again soon.

感謝大家今天的參與。感謝您的時間，我們期待很快再次與您交談。

This concludes today's conference call. Thank you for your participation and you may now disconnect.

今天的電話會議到此結束。感謝您的參與，您現在可以斷開連接。